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Tensor signal processing is an emerging field with important applications to computer vision and image processing. However, tensor applications and tensor-processing tools arise from very different areas, and these advances are too often kept within the areas of knowledge where they were first employed. This book presents the state of the art in this new branch of signal processing, offering research and discussion by leading experts in the area. The broad coverage supplies an overview of cutting-edge research into the newest tensor-processing techniques and their application to different domains related to computer vision and image processing. The contents demonstrate how new challenges in computer vision and image processing lead to new procedures for dealing with tensors, which are more related to the tensor nature of the information itself than to a specific application. The book provides a unique perspective on tensor analysis that encompasses concepts from traditionally disparate areas of mathematics, physics and engineering, with a particular focus on practical applications. Topics and features: Describes the use of tensors and tensor field processing in a number of different applications Examines spherical tensor calculus for local adaptive filtering and geometric transformations of local structure tensors Contains contributions by internationally renowned authorities in the field Discusses the use of tensors in computer vision applications, such as camera models and multilinear applications Presents an entire section on medical imaging, describing applications from Diffusion Tensor Magnetic Resonance Imaging to strain tensor estimation in cardiac analysis and elastography imaging Explores issues of storage, visualization and interfaces with tensors This comprehensive text is an invaluable reference and resource for researchers, practitioners and advanced students working in the area of computer vision and image processing. Dr Santiago Aja-Fernández and Dr Rodrigo de Luis García are Associate Professors of Telecommunications Engineering at the Universidad de Valladolid. Dr Dacheng Tao is Nanyang Assistant Professor with the School of Computer Engineering at the Nanyang Technological University. Dr Xuelong Li is Senior Lecturer at Birkbeck, University of London.

Computer vision. --- Image processing. --- Signal processing. --- Tensor products. --- Image processing --- Tensor products --- Computer vision --- Signal processing --- Applied Physics --- Engineering & Applied Sciences --- Calculus of tensors. --- Mathematics. --- Pictorial data processing --- Picture processing --- Processing, Image --- Machine vision --- Vision, Computer --- Absolute differential calculus --- Analysis, Tensor --- Calculus, Absolute differential --- Calculus, Tensor --- Tensor analysis --- Tensor calculus --- Computer science. --- Computer graphics. --- Computer Science. --- Image Processing and Computer Vision. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Imaging systems --- Optical data processing --- Artificial intelligence --- Pattern recognition systems --- Geometry, Differential --- Geometry, Infinitesimal --- Vector analysis --- Spinor analysis --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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This unique text/reference presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and varied estimation and filtering techniques drawn from more than ten years of research in this area. Topics and features: Provides a complete framework for the modeling and analysis of noise in MRI, considering different modalities and acquisition techniques Describes noise and signal estimation for MRI from a statistical signal processing perspective Surveys the different methods to remove noise in MRI acquisitions, under different approaches and from a practical point of view Reviews different techniques for estimating noise from MRI data in single- and multiple-coil systems for fully sampled acquisitions Examines the issue of noise estimation when accelerated acquisitions are considered, and parallel imaging methods are used to reconstruct the signal Includes appendices covering probability density functions, combinations of random variables used to derive estimators, and useful MRI datasets This practically-focused work serves as a reference manual for researchers dealing with signal processing in MRI acquisitions, and is also suitable as a textbook for postgraduate students in engineering with an interest in medical image processing. Dr. Santiago Aja-Fernández is an Associate Professor at the School of Telecommunications of the University of Valladolid, Spain. His other publications include the Springer title Tensors in Image Processing and Computer Vision. Dr. Gonzalo Vegas-Sánchez-Ferrero is a Research Fellow at Brigham and Women’s Hospital, and in the Applied Chest Imaging Laboratory of Harvard Medical School, Boston, MA, USA.

Statistical science --- Operational research. Game theory --- Mathematical statistics --- Biomathematics. Biometry. Biostatistics --- Human biochemistry --- Computer science --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- MRI (magnetic resonance imaging) --- DIP (documentimage processing) --- beeldverwerking --- medische biochemie --- medische statistiek --- stochastische analyse --- vormgeving --- biochemie --- biostatistiek --- computers --- statistiek --- mineralen (chemie) --- simulaties --- mijnbouw --- biometrie --- informatietechnologie --- parallel processing --- computerkunde

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Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- beeldverwerking --- image processing --- grafische vormgeving